Infrared light sensors permit rapid recording of wingbeat frequency and bioacoustic species identification of mosquitoes

Effect of physiological and environmental factors on mosquito wingbeat frequency

Wingbeat frequency may serve as a distinctive physical signature for identifying mosquito species. However, variation in wingbeat frequency within species may compromise reliability of wingbeat frequency-based mosquito identification. We examined the impact of mosquito density (number of females), time of day (day or night), gravid status, and age (days post-emergence) on the wingbeat frequency of three important vector mosquito species using infrared optical sensors. Wingbeat frequency of Culex quinquefasciatus was significantly higher (6.2% and 9.5%) for single females compared to groups of five and ten females, respectively, and 9.4% higher during the daytime compared to nighttime. Wingbeat frequency was also significantly higher for gravid Cx. quinquefasciatus (9.4%) and Aedes aegypti (1.4%) than nongravid conspecifics. Within a short age range (five to seven days post-emergence), wingbeat in Cx. quinquefasciatus did not vary significantly. Our results highlight that both extrinsic (density and time of day) and intrinsic (gravid status) factors contribute to wingbeat variation, potentially posing challenges for development of wingbeat profile libraries and the classification of unknown specimens. Traps that aim to use wingbeat frequency and target specific cohorts of the population (i.e., host-seeking or gravid females) will need to account for differences in wingbeat frequency due to multiple factors.

Smart technology for mosquito control: Recent developments, challenges, and future prospects

Smart technology coupled with digital sensors and deep learning networks have emerging scopes in various fields, including surveillance of mosquitoes. Several studies have been conducted to examine the efficacy of such technologies in the differential identification of mosquitoes with high accuracy. Some smart trap uses computer vision technology and deep learning networks to identify live Aedes aegypti and Culex quinquefasciatus in real time. Implementing such tools integrated with a reliable capture mechanism can be beneficial in identifying live mosquitoes without destroying their morphological features. Such smart traps can correctly differentiates between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak. Smart devices embedded with YOLO V4 Deep Neural Network algorithm has been designed with a differential drive mechanism and a mosquito trapping module to attract mosquitoes in the environment. The use of acoustic and optical sensors in combination with machine learning techniques have escalated the automatic classification of mosquitoes based on their flight characteristics, including wing-beat frequency. Thus, such Artificial Intelligence-based tools have promising scopes for surveillance of mosquitoes to control vector-borne diseases. However working efficiency of such technologies requires further evaluation for implementation on a global scale.

Monitoring Mosquito Abundance: Comparing an Optical Sensor with a Trapping Method

Optical sensors have shown significant promise in offering additional data to track insect populations. This article presents a comparative study between abundance measurements obtained from a novel near-infrared optical sensor and physical traps. The optical instrument, named an Entomological Bistatic Optical Sensor System, or eBoss, is a non-destructive sensor operating in the near-infrared spectral range and designed to continuously monitor the population of flying insects. The research compares the mosquito aerial density (#/m3) obtained through the eBoss with trap counts from eight physical traps during an eight-month field study. The eBoss recorded over 302,000 insect sightings and assessed the aerial density of all airborne insects as well as male and female mosquitoes specifically with a resolution of one minute. This capability allows for monitoring population trends throughout the season as well as daily activity peaks. The results affirmed the correlation between the two methods. While optical instruments do not match traps in terms of taxonomic accuracy, the eBoss offered greater temporal resolution (one minute versus roughly three days) and statistical significance owing to its much larger sample size. These outcomes further indicate that entomological optical sensors can provide valuable complementary data to more common methods to monitor flying insect populations, such as mosquitoes or pollinators.

Differences in male Aedes aegypti and Aedes albopictus hearing systems facilitate recognition of conspecific female flight tones

When Aedes albopictus mosquitoes invade regions predominated by Aedes aegypti, either the latter can be displaced or the species can coexist, with potential consequences on disease transmission. Males from both species identify females by listening for her flight sounds. Comparing male hearing systems may provide insight into how hearing could prevent interspecific mating. Here, we show that species-specific differences in female wing beat frequencies are reflected in differences in male ear mechanical tuning frequencies and sound response profiles. Though Aedes albopictus males are attracted to sound, they do not readily display abdominal bending, unlike Aedes aegypti. We observed interspecific differences in male ear mechanical, but not electrical, tuning, suggesting a conserved primary auditory processing pathway. Our work suggests a potential role for hearing in the premating isolation of Aedes aegypti and Aedes albopictus, with implications for predicting future dynamics in their sympatric relationships and our understanding of mosquito acoustic communication.

Automated differentiation of mixed populations of free-flying female mosquitoes under semi-field conditions

Great advances in automated identification systems, or 'smart traps', that differentiate insect species have been made in recent years, yet demonstrations of field-ready devices under free-flight conditions remain rare. Here, we describe the results of mixed-species identification of female mosquitoes using an advanced optoacoustic smart trap design under free-flying conditions. Point-of-capture classification was assessed using mixed populations of congeneric (Aedes albopictus and Aedes aegypti) and non-congeneric (Ae. aegypti and Anopheles stephensi) container-inhabiting species of medical importance. Culex quinquefasciatus, also common in container habitats, was included as a third species in all assessments. At the aggregate level, mixed collections of non-congeneric species (Ae. aegypti, Cx. quinquefasciatus, and An. stephensi) could be classified at accuracies exceeding 90% (% error = 3.7-7.1%). Conversely, error rates increased when analysing individual replicates (mean % error = 48.6; 95% CI 8.1-68.6) representative of daily trap captures and at the aggregate level when Ae. albopictus was released in the presence of Ae. aegypti and Cx. quinquefasciatus (% error = 7.8-31.2%). These findings highlight the many challenges yet to be overcome but also the potential operational utility of optoacoustic surveillance in low diversity settings typical of urban environments.

Chasing Flies: The Use of Wingbeat Frequency as a Communication Cue in Calyptrate Flies (Diptera: Calyptratae)

The incidental sound produced by the oscillation of insect wings during flight provides an opportunity for species identification. Calyptrate flies include some of the fastest and most agile flying insects, capable of rapid changes in direction and the fast pursuit of conspecifics. This flight pattern makes the continuous and close recording of their wingbeat frequency difficult and limited to confined specimens. Advances in sound editor and analysis software, however, have made it possible to isolate low amplitude sounds using noise reduction and pitch detection algorithms. To explore differences in wingbeat frequency between genera and sex, 40 specimens of three-day old Sarcophaga crassipalpis, Lucilia sericata, Calliphora dubia, and Musca vetustissima were individually recorded in free flight in a temperature-controlled room. Results showed significant differences in wingbeat frequency between the four species and intersexual differences for each species. Discriminant analysis classifying the three carrion flies resulted in 77.5% classified correctly overall, with the correct classification of 82.5% of S. crassipalpis, 60% of C. dubia, and 90% of L. sericata, when both mean wingbeat frequency and sex were included. Intersexual differences were further demonstrated by male flies showing significantly higher variability than females in three of the species. These observed intergeneric and intersexual differences in wingbeat frequency start the discussion on the use of the metric as a communication signal by this taxon. The success of the methodology demonstrated differences at the genus level and encourages the recording of additional species and the use of wingbeat frequency as an identification tool for these flies.

A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

Background

Every year, more than 700,000 people die from vector-borne diseases, mainly transmitted by mosquitoes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxonomical identification. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classification of mosquitoes based on their flight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the field, which could lead to significant improvements in vector surveillance.

Methods

A novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classification of mosquitoes by genus and sex. Recordings of > 4300 laboratory-reared mosquitoes of Aedes and Culex genera were made using the sensor. The chosen genera include mosquito species that have a major impact on public health in many parts of the world. Five features were extracted from each recording to form balanced datasets and used for the training and evaluation of five different machine learning algorithms to achieve the best model for mosquito classification.

Results

The best accuracy results achieved using machine learning were: 94.2% for genus classification, 99.4% for sex classification of Aedes, and 100% for sex classification of Culex. The best algorithms and features were deep neural network with spectrogram for genus classification and gradient boosting with Mel Frequency Cepstrum Coefficients among others for sex classification of either genus.

Conclusions

To our knowledge, this is the first time that a sensor coupled to a standard mosquito suction trap has provided automatic classification of mosquito genus and sex with high accuracy using a large number of unique samples with class balance. This system represents an improvement of the state of the art in mosquito surveillance and encourages future use of the sensor for remote, real-time characterization of mosquito populations.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05324-5.